mycelia

6 ms·

1.
by mycelia 8mo ago
For my work personally, agentic AI usage is pretty standard SWE fare (Cursor/CC). Even within the engine, optimizations are often centered around things like increasing communication/compute overlap (this is called Dual-Batch Over
2.
by mycelia 8mo ago
Lot of cool stuff coming up! As a Ray developer, I focus more on the orchestration layer, so I'm excited about things like Elastic Expert Parallelism, posttraining enhancements like colocated trainer/engines, and deploying DSV4 (r
3.
by mycelia 8mo ago
Hi! This benchmarking was done w/ DeepSeek-V3's published FP8 weights. And Blackwell performance is still being optimized. SGLang hit 14k/s/B200 though, pretty cool writeup here: https://lmsys.org/blog&#x
4.
by mycelia 8mo ago
Hey HN! I’m Seiji Eicher from Anyscale, one of the authors of this post :) Feel free to ask questions here.
5.
LLM Inference with Ray: Expert parallelism and prefill/decode disaggregation(anyscale.com)
1 points by mycelia 10mo ago|0 comments
6.
LLM Engine Orchestration for Performance(anyscale.com)
1 points by mycelia 1y ago|0 comments
7.
by mycelia 4y ago
I’d be curious to see the salary distributions for SWE and PMs. My guess would be that SWE has a greater range and variance, since for example (according to Blind) target compensation for Meta PMs is a percentage (something like .8) of SWE
8.
by mycelia 4y ago
https://web.archive.org/web/20220710191240/https://www.econo...
9.
by mycelia 4y ago
Yes, I believe CS 106A would be more analogous to Harvard’s CS 50. https://web.stanford.edu/class/archive/cs/cs106a/cs106a.1228...